Caregiving concerns and clinical characteristics across neurodegenerative and cerebrovascular disorders in the Ontario neurodegenerative disease research initiative

Objectives: Caregiving burdens are a substantial concern in the clinical care of persons with neurodegenerative disorders. In the Ontario Neurodegenerative Disease Research Initiative, we used the Zarit\u27s Burden Interview (ZBI) to examine: (1) the types of burdens captured by the ZBI in a cross-disorder sample of neurodegenerative conditions (2) whether there are categorical or disorder-specific effects on caregiving burdens, and (3) which demographic, clinical, and cognitive measures are related to burden(s) in neurodegenerative disorders?. Methods/Design: N = 504 participants and their study partners (e.g., family, friends) across: Alzheimer\u27s disease/mild cognitive impairment (AD/MCI; n = 120), Parkinson\u27s disease (PD; n = 136), amyotrophic lateral sclerosis (ALS; n = 38), frontotemporal dementia (FTD; n = 53), and cerebrovascular disease (CVD; n = 157). Study partners provided information about themselves, and information about the clinical participants (e.g., activities of daily living (ADL)). We used Correspondence Analysis to identify types of caregiving concerns in the ZBI. We then identified relationships between those concerns and demographic and clinical measures, and a cognitive battery. Results: We found three components in the ZBI. The first was “overall burden” and was (1) strongly related to increased neuropsychiatric symptoms (NPI severity r = 0.586, NPI distress r = 0.587) and decreased independence in ADL (instrumental ADLs r = −0.566, basic ADLs r = −0.43), (2) moderately related to cognition (MoCA r = −0.268), and (3) showed little-to-no differences between disorders. The second and third components together showed four types of caregiving concerns: current care of the person with the neurodegenerative disease, future care of the person with the neurodegenerative disease, personal concerns of study partners, and social concerns of study partners. Conclusions: Our results suggest that the experience of caregiving in neurodegenerative and cerebrovascular diseases is individualized and is not defined by diagnostic categories. Our findings highlight the importance of targeting ADL and neuropsychiatric symptoms with caregiver-personalized solutions

The use of random forests to classify amyloid brain PET

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB. Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

The utility of multivariate outlier detection techniques for data quality evaluation in large studies: an application within the ONDRI project

Abstract Background Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow. Methods We illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods – Minimum Covariance Determinant (MCD) and Candès’ Robust Principal Component Analysis (RPCA) – and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification. Results Of 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection. Conclusions Manual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex

DOC-Apnea scores to impairment on the polysomnogram.

<p>DOC-Apnea scores to impairment on the polysomnogram.</p